Population-based continuous optimization, probabilistic modelling and mean shift

Gallagher, M. and Frean, M. (2005) Population-based continuous optimization, probabilistic modelling and mean shift. Evolutionary Computation, 13 1: 29-42. doi:10.1162/1063656053583478

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Author Gallagher, M.
Frean, M.
Title Population-based continuous optimization, probabilistic modelling and mean shift
Journal name Evolutionary Computation   Check publisher's open access policy
ISSN 1063-6560
1530-9304
Publication date 2005-01-01
Sub-type Article (original research)
DOI 10.1162/1063656053583478
Volume 13
Issue 1
Start page 29
End page 42
Total pages 14
Editor M. Schoenaver
P. Larranaga
J. A. Lozano
Place of publication Cambridge, MA, United States
Publisher MIT Press
Collection year 2005
Language eng
Abstract Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.
Keyword Computer Science, Theory & Methods
Probabilistic Modelling
Estimation Of Distribution Algorithms
Population-based Incremental Learning
Mean Shift
Continuous Optimization
Computer Science, Artificial Intelligence
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 17:07:18 EST